Causal Discovery in Financial Markets: A Framework for Nonstationary Time-Series Data

ArXiv ID: 2312.17375 “View on arXiv”

Authors: Unknown

Abstract

This paper introduces a new causal structure learning method for nonstationary time series data, a common data type found in fields such as finance, economics, healthcare, and environmental science. Our work builds upon the constraint-based causal discovery from nonstationary data algorithm (CD-NOD). We introduce a refined version (CD-NOTS) which is designed specifically to account for lagged dependencies in time series data. We compare the performance of different algorithmic choices, such as the type of conditional independence test and the significance level, to help select the best hyperparameters given various scenarios of sample size, problem dimensionality, and availability of computational resources. Using the results from the simulated data, we apply CD-NOTS to a broad range of real-world financial applications in order to identify causal connections among nonstationary time series data, thereby illustrating applications in factor-based investing, portfolio diversification, and comprehension of market dynamics.

Keywords: causal discovery, nonstationary time series, conditional independence test, CD-NOTS, factor-based investing, Multi-Asset

Complexity vs Empirical Score

• Math Complexity: 8.0/10
• Empirical Rigor: 7.0/10
• Quadrant: Holy Grail
• Why: The paper presents advanced mathematical concepts such as conditional independence tests (KCIT, RCoT, CMIknn) and nonparametric causal discovery algorithms, requiring high mathematical density. It demonstrates strong empirical rigor by evaluating the algorithm on simulated and real-world financial data, addressing implementation details like hyperparameter selection and computational resource considerations.

  flowchart TD
    A["Research Goal: <br>Discover causal structures in <br>nonstationary financial time-series data"] --> B["Data Input:<br>Simulated & Real Financial Data"]
    B --> C["Core Methodology:<br>CD-NOTS Algorithm<br>(Lagged Dependencies)"]
    C --> D["Hyperparameter Tuning:<br>Conditional Independence Tests & Significance"]
    D --> E["Validation:<br>Performance on Simulated Data"]
    E --> F["Key Findings & Outcomes:"]
    F --> G["Factor-based Investing<br>Portfolio Diversification<br>Market Dynamics"]